In Table 8, we compare our method with other recent state-of-the-art methods. The hand-crafted features were extracted in [1]}, [2]}. The methods which have been compared on the ShanghaiTech are still compared in this dataset. The detailed comparison results are shown in Table 8. In the same way, it can be observed that all of the CNN-based methods outperform the methods of traditional feature extraction [1]}, [2]} significantly. The proposed SegCrowdNet leads the best performance in MAE. More specifically, its MAE is 38.0 lower than the second best method. However, the MSE which indicates the robustness of a method is not the best. We think that the root of causing this result lies in the limited data, as there are only 50 images in this dataset.
